Abstract
The accelerated failure time (AFT) model, also called censored linear regression has played a central role in survival analysis. Motivated by (Zhao, Stat Probab Lett 81:603bab, 2011), we make an empirical likelihood (EL) inference for the model using the monotone censored Kendall’s rank-estimating equation. The limiting distribution of the EL ratio follows the Wilks theorem. In addition, we carry out extensive simulation studies to compare the EL for the Kendall’s rank-regression estimator with Wald-type and EL interval estimators. The simulation shows the benefit of the proposed method for small sample sizes in most cases.
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Acknowledgments
The authors are grateful to the reviewer for the useful comments. The authors would like to thank Maxime Boudoumou and Jenny Zhao for their help. Yichuan Zhao acknowledges partial support from the NSA grant and the IRG grant in the Georgia State University.
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Lu, Y., Zhao, Y. (2015). Empirical Likelihood for the AFT Model Using Kendall’s Rank Estimating Equation. In: Chen, Z., Liu, A., Qu, Y., Tang, L., Ting, N., Tsong, Y. (eds) Applied Statistics in Biomedicine and Clinical Trials Design. ICSA Book Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-12694-4_18
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DOI: https://doi.org/10.1007/978-3-319-12694-4_18
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